Earticle

초록

영어

The short-term load forecasting is an important method for security dispatching and economical operation in electric power system, and its prediction accuracy directly affects the operating reliability of the electric system. So the global optimization ability of particle swarm optimization (PSO) algorithm and classification prediction ability of support vector machine (SVM) are combined in order to realize the mutual supplement with each other's advantages in this paper. Firstly, the PSO algorithm is used to optimize the parameters of the SVM in order to obtain the optimal parameters of the SVM. Then a short-term load forecasting method based on combining the PSO and SVM according to the characteristics and influencing factors of short-term load forecasting is proposed. An actual power system in one region is applied to test and verify the short-term load forecasting method. The results show that the short-term load forecasting method takes on the good convergence and higher prediction precision.

목차

Abstract
 1. Introduction
 2. Basic Method
  2.1. Particle Swarm Optimization Algorithm
  2.2. Particle Swarm Optimization Algorithm
 3. The Optimized SVM Model Based On PSO Algorithm
  3.1. The Selection of Kernel Function
  3.2. The Determined Parameters of SVM Model
 4. The Short-Term Load Forecasting Method Based On PSO and SVM
 5. Conclusion
 Acknowledgements
 References

키워드

저자

• Dao Jiang [ School of Electronic and Information Engineering, Shunde Polytechnic, Shunde 528000 China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Control and Automation

• 간기

  월간

• pISSN

  2005-4297

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605